Techniques for service execution and monitoring for run-time service composition

ABSTRACT

A server system may receive two or more Quality of Service (QoS) dimensions for the multi-objective optimization model, wherein the two or more QoS dimensions include at least a first QoS dimension and a second QoS dimension. The server system may maximize the multi-objective optimization model along the first QoS dimension, wherein the maximizing includes selecting one or more pipelines for the multi-objective optimization model in the software architecture that meet QoS expectations specified for the first QoS dimension and the second QoS dimension, wherein an ordering of the pipelines is dependent on which QoS dimensions were optimized and de-optimized and to what extent, wherein the multi-objective optimization model is partially de-optimized along the second QoS dimension in order to comply with the QoS expectations for the first QoS dimension, and whereby there is a tradeoff between the first QoS dimension and the second QoS dimension.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority of U.S. Provisional Patent ApplicationNo. 62/900,537 filed Sep. 14, 2019, entitled “AUTOMATED MACHINE-LEARNINGSYSTEMS AND METHODS”, which is hereby incorporated by reference in itsentirety and for all purposes.

FIELD

The present disclosure relates to systems and techniques for machinelearning. More particularly, the present disclosure relates to systemsand techniques for generating and managing a library of machine-learningapplications.

BACKGROUND

Machine-learning has a wide range of applications, such as searchengines, medical diagnosis, text and handwriting recognition, imageprocessing and recognition, load forecasting, marketing and salesdiagnosis, chatbots, autonomous driving, and the like. Various types andversions of machine-learning models may be generated for similarapplications using training data based on different technologies,languages, libraries, and the like, and thus may lack interoperability.In addition, different models may have different performances indifferent contexts and/or for different types of input data. Datascientists may not have the programming skills to generate the codenecessary to build custom machine-learning models. In addition,available machine-learning tools do not store the variousmachine-learning model components as part of a library to allow forefficient reuse of routines in other machine-learning models.

Existing machine-learning applications can require considerableprogramming knowledge by a data scientist to design and construct amachine-learning application to solve specific problems. Intuitiveinterfaces can assist the data scientist construct a machine-learningapplication through a series of queries.

Some organizations can store data from multiple clients or supplierswith customizable schemas. These customizable schemas may not matchstandardized data storage schemas used by existing machine-learningmodels. Therefore, these other systems would need to perform areconciliation process prior to using the stored data. Thereconciliation process can be either a manual process or through atedious extract, transform, load automated process prior to using thedata for generating machine-learning applications.

Machine-learning applications based only on metrics (e.g., Quality ofService (QoS) or Key Performance Indicators) may not be sufficient tocompose pipelines with minimal human intervention for a self-adaptivearchitecture. Pre-existing machine-learning tools do not combinenon-logical based and logic-based semantic services to generate amachine-learning application.

One or more changes due to: changes in the system environment, datacorruption, concept drift, and availability of new data can affect theoutcome of the machine-learning model. Existing machine-learningapplications do not predict the effects of these changes orautomatically identify and execute remedial measures to mitigate forthese changes.

BRIEF SUMMARY

Certain aspects and features of the present disclosure relate tomachine-learning platform that generates a library of components togenerate machine-learning models and machine-learning applications. Themachine-learning infrastructure system allows a user (i.e., a datascientist) to generate machine-learning applications without havingdetailed knowledge of the cloud-based network infrastructure orknowledge of how to generate code for building the model. Themachine-learning platform can analyze the identified data and the userprovided desired prediction and performance characteristics to selectone or more library components and associated application-programminginterface (API) to generate a machine-learning application. Themachine-learning techniques can monitor and evaluate the outputs of themachine-learning model to allow for feedback and adjustments to themodel. The machine-learning application can be trained, tested, andcompiled for export as stand-alone executable code.

The machine-learning platform can generate and store one or more librarycomponents that can be used for other machine-learning applications. Themachine-learning platform can allow users to generate a profile whichallows the platform to make recommendations based on a user's historicalpreferences. The model creation engine can detect the number and type ofinfrastructure resources necessary to achieve the desired results withinthe desired performance criteria.

According to some implementations, a method may include receiving two ormore Quality of Service (QoS) dimensions for the multi-objectiveoptimization model. The two or more QoS dimensions include at least afirst QoS dimension and a second QoS dimension. The method may includemaximizing the multi-objective optimization model along the first QoSdimension. The maximizing includes selecting one or more pipelines forthe multi-objective optimization model in the software architecture thatmeet QoS expectations specified for the first QoS dimension and thesecond QoS dimension. An ordering of the pipelines can be dependent onwhich QoS dimensions were optimized and de-optimized and to what extent.The multi-objective optimization model can be partially de-optimizedalong the second QoS dimension in order to comply with the QoSexpectations for the first QoS dimension. Whereby there is a tradeoffbetween the first QoS dimension and the second QoS dimension.

According to some implementations, a non-transitory computer-readablemedium may store one or more instructions. The one or more instructions,when executed by one or more processors of a server system, may causethe one or more processors to: receiving two or more Quality of Service(QoS) dimensions for the multi-objective optimization model, wherein thetwo or more QoS dimensions include at least a first QoS dimension and asecond QoS dimension; maximizing the multi-objective optimization modelalong the first QoS dimension, wherein the maximizing includes selectingone or more pipelines for the multi-objective optimization model in thesoftware architecture that meet QoS expectations specified for the firstQoS dimension and the second QoS dimension, wherein an ordering of thepipelines is dependent on which QoS dimensions were optimized andde-optimized and to what extent, wherein the multi-objectiveoptimization model is partially de-optimized along the second QoSdimension in order to comply with the QoS expectations for the first QoSdimension, and whereby there is a tradeoff between the first QoSdimension and the second QoS dimension.

According to some implementations, a method may include retrieving dataassociated with a historical output of a machine-learning model ascompared with a set of Quality of Service metrics and Key PerformanceIndicator Metrics. The method can include receiving one or more inputsfrom an environment monitoring agent. The environment monitoring agentcan receive information on at least one of: resources of a system,concepts of the machine-learning model, data corruption, and dataavailability to the machine-learning model. The method can includedetermining a change in at least one of: the resources of the system,the concepts of the machine-learning model, the data corruption, and thedata availability to the machine-learning model. The method can includedetermining whether the change in the at least one of the resources ofthe system, the concepts of the machine-learning model, the datacorruption, and the data availability to the machine-learning model willcause a predicted output of the machine-learning model to vary more thana predetermined amount. When the change in the at least one of theresources of the system, the concepts of the machine-learning model, thedata corruption, and the data availability to the machine-learning modelwill cause the predicted output of the machine-learning model to varymore than a predetermined amount, the method can include identifying oneor more remedial measures to the machine-learning model to correct forthe change. The method can include displaying an alert to notify a userof the change in the at least one of the resources of the system, theconcepts of the machine-learning model, the data corruption, and thedata availability to the machine-learning model and the one or moreremedial measures.

According to some implementations, a non-transitory computer-readablemedium may store one or more instructions. The one or more instructions,when executed by one or more processors of a cloud-based server system,may cause the one or more processors to: retrieve data associated with ahistorical output of a machine-learning model as compared with a set ofQuality of Service metrics and Key Performance Indicator Metrics. Theinstructions can cause the one or more processors to receive one or moreinputs from an environment monitoring agent. The environment monitoringagent receives information on at least one of: resources of a system,concepts of the machine-learning model, data corruption, and dataavailability to the machine-learning model. The instructions can causethe one or more processors to determine a change in at least one of: theresources of the system, the concepts of the machine-learning model, thedata corruption, and the data availability to the machine-learningmodel. The instructions can cause the one or more processors todetermine whether the change in the at least one of the resources of thesystem, the concepts of the machine-learning model, the data corruption,and the data availability to the machine-learning model will cause apredicted output of the machine-learning model to vary more than apredetermined amount. When the change in the at least one of theresources of the system, the concepts of the machine-learning model, thedata corruption, and the data availability to the machine-learning modelwill cause the predicted output of the machine-learning model to varymore than a predetermined amount, the instructions can cause the one ormore processors to identifying one or more remedial measures to themachine-learning model to correct for the change. The instructions cancause the one or more processors to display an alert to notify a user ofthe change in the at least one of the resources of the system, theconcepts of the machine-learning model, the data corruption, and thedata availability to the machine-learning model and the one or moreremedial measures.

These and other embodiments are described in detail below. For example,other embodiments are directed to systems, devices, and computerreadable media associated with methods described herein.

A better understanding of the nature and advantages of embodiments ofthe present disclosed may be gained with reference to the followingdetailed description and the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The specification makes reference to the following appended figures, inwhich use of like reference numerals in different figures is intended toillustrate like or analogous components.

FIG. 1 is a block diagram illustrating an exemplary machine-learninginfrastructure system.

FIG. 2 illustrates a diagram of a system for service execution andmonitoring for run-time service composition.

FIG. 3 illustrates an exemplary flow chart for service execution andmonitoring for run-time service composition.

FIG. 4 is a simplified diagram illustrating a distributed system forimplementing one of the embodiments.

FIG. 5 is a simplified block diagram illustrating one or more componentsof a system environment.

FIG. 6 illustrates an exemplary computer system, in which variousembodiments of the present disclosure may be implemented.

DETAILED DESCRIPTION

Certain embodiments of the present disclosure relate to systems,devices, computer-readable medium, and computer-implemented methods forimplementing various techniques for machine learning. Themachine-learning techniques can allow a user (i.e., a data scientist) togenerate machine-learning applications without having detailed knowledgeof the cloud-based network infrastructure or knowledge of how togenerate code for building the model. The machine-learning platform cananalyze the identified data and the user provided desired prediction andperformance characteristics to select one or more library components andassociated API to generate a machine-learning application.

The machine-learning techniques can employ a chatbot to indicate thelocation of data, select a type of machine-learning solution, displayoptimal solutions that best meet the constraints, and recommend the bestenvironment to deploy the solution.

The techniques described herein can include a self-adjustingcorporation-wide discovery and integration feature can review a client'sdata store, review the labels for the various data schema, andeffectively map the client's data schema to classifications used by themachine-learning model. The various techniques can automatically selectthe features that are predictive for each individual use case (i.e., oneclient), effectively making a machine-learning solution client-agnosticfor the application developer. A weighted list of common representationsof each feature for a particular machine-learning solution can begenerated and stored.

The techniques can utilize existing data ontologies for generatingmachine-learning solutions for a high-precision search of relevantservices to compose pipelines with minimal human intervention. For datasets without existing ontologies, one or more ontologies be generated.

The techniques can employ an adaptive pipelining composition service toidentify and incorporate or more new models into the machine-learningapplication. The machine-learning application with the new model can betested off-line with the results being compared with ground truth data.If the machine-learning application with the new model outperforms thepreviously used model, the machine-learning application can be upgradedand auto-promoted to production.

A cloud-based server system can include a monitoring engine that canreceive information on at least one of: resources of a system, conceptsof the machine-learning model, data corruption, and data availability tothe machine-learning model. The monitoring engine can determine whetherthe change will cause a predicted output of the machine-learning modelto vary more than a predetermined amount. The monitoring engine canidentify one or more remedial measures to the machine-learning model tocorrect for the change. In at least one embodiment, the cloud-basedserver system may display an alert to notify a user of the change in theat least one of the resources of the system, the concepts of themachine-learning model, the data corruption, and the data availabilityto the machine-learning model and the one or more remedial measures.

I. Machine-Learning Infrastructure Platform

FIG. 1 is a block diagram illustrating an exemplary machine-learningplatform 100 for generating a machine-learning model. Themachine-learning platform 100 has various components that can bedistributed between different networks and computing systems. Amachine-learning infrastructure library can store one or more componentsfor generating machine-learning applications 112. All of theinfrastructure required to productionize the machine-learningapplications 112 can be encapsulated and stored in the library.

Machine-learning configuration and interaction with the modelcomposition engine 132 allows for selection of various librarycomponents 168 (e.g., pipelines 136 or workflows, micro servicesroutines 140, software modules 144, and infrastructure modules 148) todefine implementation of the logic of training and inference to buildmachine-learning applications 112. Different parameters, variables,scaling, settings, etc. for the library components 168 can be specifiedor determined by the model composition engine 132. The complexityconventionally required to create the machine-learning applications 112can be performed largely automatically with the model composition engine132.

The library components 168 can be scalable to allows for the definitionof multiple environments (e.g., different Kubernetes clusters) where thevarious portions of the application can be deployed to achieve anyQuality of Service (QoS) or Key Performance Indicators (KPIs) specified.A Kubernetes cluster is a set of node machines for running containerizedapplications. The scalability can hide or abstract the complexity of themachine-learning platform 100 from the application developer. Amonitoring engine 156 can monitor operation of the machine-learningapplications 112 according to the KPI/QoS metrics 160 to assure themachine-learning application 112 is performing according torequirements. In addition the monitoring engine 156 can seamlessly testend-to-end a new or evolving machine-learning application at differentscales, settings, loading, settings, etc. The monitoring engine 156 canrecommend various adjustments to the machine-learning application 112 bysignaling needed changes to the model composition engine 132.

To address scalability in some embodiments, the machine-learningplatform 100 creates infrastructure, which is based on a micro servicesarchitecture, making it robust and scalable. For example, various microservices routines 140 and infrastructure modules 148 can be configuredand customized for embedding into the machine-learning application 112.The machine-learning platform 100 can allow a developer to define theamount of resources (e.g. CPU, memory) needed for different librarycomponents 168 of the machine-learning application 112.

The machine-learning platform 100 can generate highly customizableapplications. The library components 168 contain a set of predefined,off-the-shelf workflows or pipelines 136, which the applicationdeveloper can incorporate into a new machine-learning application 112. Aworkflow specifies various micro services routines 140, software modules144 and/or infrastructure modules 148 configured in a particular way fora type or class of problem. In addition to this, it is also possible todefine new workflows or pipelines 136 by re-using the library componentsor changing an existing workflow or pipeline 136. The infrastructuremodules 148 can also include services such as data gathering, processmonitoring, and logging.

A model composition engine 132 can be executed on one or more computingsystems (e.g., infrastructure 128). The model composition engine 132 canreceive inputs from a user 116 through an interface 104. The interface104 can include various graphical user interfaces with various menus anduser selectable elements. The interface 104 can include a chatbot (e.g.,a text based or voice based interface). The user 116 can interact withthe interface 104 to identify one or more of: a location of data, adesired prediction of machine-learning application, and variousperformance metrics for the machine-learning model. The modelcomposition engine 132 can interface with library components 168 toidentify various pipelines 136, micro service routines 140, softwaremodules 144, and infrastructure models 148 that can be used in thecreation of the machine-learning model 112.

The model composition engine 132 can output one or more machine-learningapplications 112. The machine-learning applications 112 can be storedlocally on a server or in a cloud-based network. The model compositionengine 132 can output the machine-learning application 112 as executablecode that be run on various infrastructure 128 through theinfrastructure interfaces 124.

The model execution engine 108 can execute the machine-learningapplication 112 on infrastructure 128 using one or more theinfrastructure interfaces 124. The infrastructure 128 can include one ormore processors, one or more memories, and one or more networkinterfaces, one or more buses and control lines that can be used togenerate, test, compile, and deploy a machine-learning application 112.In various embodiments, the infrastructure 128 can exit on a remotesystem 152 that is apart from the location of the user 116. Theinfrastructure 128 can interact with the model execution engine 108through the infrastructure interfaces 124 The model execution engine 108can input the performance characteristics (e.g., KPI/QoS metrics storage160) and the hosted input data 164. The model execution engine 108 cangenerate one or more results from the machine-learning application 112.

The KPI/QoS metrics storage 160 can store one or more metrics that canbe used for evaluating the machine-learning application 112. The metricscan include inference query metrics, performance metrics, sentimentmetrics, and testing metrics. The metrics can be received from a user116 through a user interface 104.

The monitoring engine 156 can receive the results of the model executionengine 108 and compare the results with the performance characteristics(e.g., KPI/QoS metrics 160). The monitoring engine 156 can use groundtruth data to test the machine-learning application 112 to ensure themodel can perform as intended. The monitoring engine 156 can providefeedback to the model composition engine 132. The feedback can includeadjustments to one or more variables or selected machine-learning modelused in the machine-learning model 112.

The library components 168 can include various pipelines 136, microservice routines 140, software modules 144, and infrastructure modules148. Software pipelines 136 can consist of a sequence of computingprocesses (e.g., commands, program runs, tasks, threads, procedures,etc.).

Micro services routines 140 can be used in an architectural approach tobuilding applications. As an architectural framework, micro services aredistributed and loosely coupled, to allow for changes to one aspect ofan application without destroying the entire application. The benefit tousing micro services is that development teams can rapidly build newcomponents of applications to meet changing development requirements.Micro service architecture breaks an application down into its corefunctions. Each function is called a service, and can be built anddeployed independently, meaning individual services can function (andfail) without negatively affecting the others. A micro service can be acore function of an application that runs independent of other services.By storing various micro service routines 140, the machine-learningplatform 100 can generate a machine-learning application incrementallyby identifying and selecting various different components from thelibrary components 168.

Software modules 144 can include batches of code that form part of aprogram that contains one or more routines. One or more independentlydeveloped modules make up a program. An enterprise-level softwareapplication can contain several different software modules 144, and eachmodule can serve unique and separate operations. A module interface canexpress the elements that are provided and required by the module. Theelements defined in the interface can be detectable by other modules.The implementation can contain the working code that corresponds to theelements declared in the interface. Modular programming can be relatedto structured programming and object-oriented programming, all havingthe same goal of facilitating construction of large software programsand systems by decomposition into smaller pieces. While the historicalusage of these terms has been inconsistent, “modular programming” asused herein refers to high-level decomposition of the code of an entireprogram into pieces: structured programming to the low-level code use ofstructured control flow, and object-oriented programming to the data useof objects, a kind of data structure. In object-oriented programming,the use of interfaces as an architectural pattern to construct modulesis known as interface-based programming.

Infrastructure modules 148 can include the technology stack necessary toget machine-learning algorithms into production in a stable, scalableand reliable way. A technology stack can include set of softwaresubsystems or components needed to create a complete platform such thatno additional software is needed to support applications. For example,to develop a web application the architect defines the stack as thetarget operating system, web server, database, and programming language.Another version of a software stack is operating system, middleware,database, and applications. The components of a software stack can bedeveloped by different developers independently from one another. Thestack can extend from the data science tools used to select and trainmachine-learning algorithms down to the hardware those algorithms run onand the databases and message queues from which they draw the datasets.

The machine-learning platform 100 can include one or more data storagelocations 170. The user can identify the one or more data storagelocations 170. The data storage location 170 can be local (e.g., in astorage device electrically connected to the processing circuitry andinterfaces used to generate, test, and execute the application). Invarious embodiments the data storage location 170 can be remote (e.g.,accessible through a network such as a Local Area Network or theInternet). In some embodiments, the data storage location 170 can be acloud-based server.

The data used for the machine-learning model 112 often includespersonally-identifiable information (PII), and thus, triggers certainsafeguards provided by privacy laws. One way to protect the informationcontained in the data storage 170 can be to encrypt the data using oneor more keys. Public-key cryptography, or asymmetric cryptography, is acryptographic system that uses pairs of keys: public keys which may bedisseminated widely, and private keys which are known only to the ownerof the data. The private keys can be stored in the key storage 172module to enable decrypting data for use by the machine-learningplatform 100.

The model execution engine 108 can use hosted input data 164 to executeand test the machine-learning application 112. The hosted input data 164can include a portion of the data stored at the data storage 170. Invarious embodiments, a portion of the hosted input data 164 can beidentified as testing data.

II. Service Execution and Monitoring for Run-Time Service Composition

During the execution of a machine-learning service or pipeline, theenvironment is in constant change and can therefore invalidate thedesired state defined by the user. The invalid state could includechanges in the environment, data corruption, model performancedegradation, and/or the availability of new features. One purpose of themonitoring engine 156 is to provide the model composition engine 132 andthe model execution engine 108 with an up-to-date view of the state ofthe execution environment for the machine-learning platform 100 andcomplying with the QoS specifications defined when the machine-learningservice was composed.

Machine-learning services and their ontologies are defined in deployableservice descriptions, which are used by the model composition engine 132to assemble a composite service to trigger search for the bestarchitectural model for run-time. The architectural model includes apipeline 136 specifying any microservices routines 140, software modules144, and infrastructure modules 148 along with any customizations andinterdependencies. Multiple QoS parameters (e.g., response time,latency, throughput, reliability, availability, success rate) asassociated with a service execution based also on the type of datainputted in the pipeline (volume, velocity), class of pipelines(classifier, recommender system), thereby, service composition with alarge number of candidate services is a multi-objective optimizationproblem that we could solve to automate the run-time adaption. Duringservice composition, multiple services can combined in a specific orderbased on their input-output dependencies to produce a desired productgraph that besides providing a solution required by a pipeline X withData Input Y, it is also necessary to ensure fulfillment of end-to-endQoS requirements specified by the product team (KPIs) and theenvironment we are running. An Execution Engine schedules and invokesmachine-learning service instances to be composed and served atrun-time.

A number of variations and modifications of the disclosed embodimentscan also be used. For example, various functions, blocks, and/orsoftware can be distributed over a network, WAN and/or cloudencapsulated. The machine-learning software can be run in a distributedfashion also across a network, WAN and/or cloud infrastructure.

FIG. 2 illustrates a simplified diagram of a system for serviceexecution and monitoring for run-time service composition. The systemcan detect when one or more conditions exist that can degrade theperformance of the machine-learning model. The system can identify oneor more measures that can be taken that can prevent, mitigate, orresolve any issues caused by a change in the at least one of theresources of the system, the concepts of the machine-learning model, thedata corruption, and the data availability to the machine-learningmodel.

The model-monitoring agent 202 can monitor the environment of the systemand the performance of the machine-learning model. The model-monitoringagent 202 can monitor both the historical performance of the model andthe performance of the model as compared with the Key PerformanceIndicators (KPIs) and Quality of Service metrics.

The model-monitoring agent 202 can monitor for concept drift. In thereal world concepts are often not stable but change with time. Typicalexamples of this are weather prediction rules and customers'preferences. The underlying data distribution may change as well. Oftenthese changes make the model built on old data inconsistent with the newdata, and regular updating of the model is necessary. This problem, canbe known as concept drift, complicates the task of learning a model fromdata and requires special approaches, different from commonly usedtechniques, which treat arriving instances as equally importantcontributors to the final concept. The monitoring engine 202 can monitorthe customer data to detect if concept drift is a potential issue forthe machine-learning application.

The model-monitoring agent 202 can monitor for data corruption. Datacorruption refers to errors in computer data that occur during writing,reading, storage, transmission, or processing, which introduceunintended changes to the original data. Computer, transmission, andstorage systems can use a number of measures to provide end-to-end dataintegrity, or lack of errors. In general, when data corruption occurs afile containing that data can produce unexpected results when accessedby the system or the related application. Results could range from aminor loss of data to a system crash. For example, if a document file iscorrupted, when a person tries to open that file with a document editorthey may get an error message, thus the file might not be opened ormight open with some of the data corrupted (or in some cases, completelycorrupted, leaving the document unintelligible). The model-monitoringagent 202 can monitor the data and detect potential issues with datacorruption.

The model-monitoring agent 202 can monitor for new customer data. Thenew customer data can include different types of data that may have beenpreviously unavailable to the model. The model-monitoring agent 202 cannot only detect the presence of additional data of the same type used bythe model, but it can detect new types of data that may provide forbetter predictions.

The model-monitoring agent 202 can notify a user of model degradation orif KPIs are not being met or are not currently capable of being met.

An external monitoring agent 204 can detect one or more environmentchanges. The environment changes can include changes to availablememory. The environment changes can include changes to the availabilityof processing nodes. The environment changes can include changes to thenetwork bandwidth.

The external monitoring agent 204 can access historical data 206. Thehistorical data 206 can be used to compare the projected output of themodel. If the projected output is within an acceptable range of thehistorical data, perhaps no remedial measures need to be taken. It ispossible that the redial measures would result in other issues that arepotentially worse than any changes to the system. The externalmonitoring agent can also save the output of the model to the databasefor future monitoring. In various embodiments, the historical data 206can store a historical collection of problems and the solutions orremedial actions given to them. This would lean the applicationdifferently, towards making adjusting running a machine-learning model,at 208, to get predicted remedial actions.

At 208, the system can adjust the running model. The system can make oneor more changes to prevent, mitigate, or resolve any issues presented bythe system changes detected by the model-monitoring agent 202 and theexternal monitoring agent 204.

For some environment changes, the system can replace processing, memory,or bandwidth expensive transformations. Other remedial measures caninclude replacing or pruning one or more model parameters. The reducedmodel parameters reduce the requirements for the model. In variousembodiments, the system can reduce the model complexity by changing outone or more of the library components.

For data corruption, the system can temporarily drop or remove thecorrupted feature. In various embodiments, the system can adjustpipeline/or layers to remove the corrupted features.

For concept drift issues, the system can force-retraining of the modelusing the different data. In various embodiments, the different datainclude the latest data. In various embodiments, force concept driftissues can be resolved by changing the size of window selections toavoid corrupted data.

For cases of new data becoming available, the system can analyze thedata to discover one or more new features. The system can evaluate theimpact of the new data on the model metrics. In various embodiments, thesystem can discard any bias due to sensitive attributes.

In some embodiments the system can roll back to a previous model version210 to mitigate one or more issues detected. In some embodiments, thesystem can use one or more model components 212 to compose a new modelpipeline, replace model microservices with different components. Thesystem can save metadata regarding the current running model. Themetadata can be monitored by the external monitoring agent 204.

FIG. 3 illustrates an exemplary flow chart for service execution andmonitoring for run-time service composition.

FIG. 3 is a flow chart of an example process 300 for techniques forservice execution and monitoring for run-time service composition. Insome implementations, one or more process blocks of FIG. 3 can beperformed by a server system (e.g., a cloud-based server system). Insome implementations, one or more process blocks of FIG. 3 can beperformed by another device or a group of devices separate from orincluding the cloud-based server.

At 310, process 300 can include receiving two or more Quality of Service(QoS) dimensions for the multi-objective optimization model, wherein thetwo or more QoS dimensions include at least a first QoS dimension and asecond QoS dimension. For example, the server system (e.g., usingprocessing unit 604, storage subsystem 618, system memory 610,communication subsystem 624, bus 602 and or data feeds 624 and/or thelike as illustrated in FIG. 6 and described below) can receive two ormore Quality of Service (QoS) dimensions for the multi-objectiveoptimization model, as described above. In some implementations, the twoor more QoS dimensions include at least a first QoS dimension and asecond QoS dimension.

At 320, process 300 can include maximizing the multi-objectiveoptimization model along the first QoS dimension. For example, theserver system (e.g., using processing unit 604, storage subsystem 618,system memory 610, communication subsystem 624, bus 602 and or datafeeds 624 and/or the like as illustrated in FIG. 6 and described below)can maximize the multi-objective optimization model along the first QoSdimension, as described above.

At 330, the maximizing can include selecting one or more pipelines forthe multi-objective optimization model in the software architecture thatmeet QoS expectations specified for the first QoS dimension and thesecond QoS dimension. For example, the server system (e.g., usingprocessing unit 604, storage subsystem 618, system memory 610,communication subsystem 624, bus 602 and or data feeds 624 and/or thelike as illustrated in FIG. 6 and described below) can include selectingone or more pipelines for the multi-objective optimization model in thesoftware architecture that meet QoS expectations specified for the firstQoS dimension and the second QoS dimension.

At 340, an ordering of the pipelines is dependent on which QoSdimensions were optimized and de-optimized and to what extent. Forexample, the server system (e.g., using processing unit 604, storagesubsystem 618, system memory 610, communication subsystem 624, bus 602and or data feeds 624 and/or the like as illustrated in FIG. 6 anddescribed below) can include ordering of the pipelines is dependent onwhich QoS dimensions were optimized and de-optimized and to what extent.

At 350, the multi-objective optimization model is partially de-optimizedalong the second QoS dimension in order to comply with the QoSexpectations for the first QoS dimension. For example, the server system(e.g., using processing unit 604, storage subsystem 618, system memory610, communication subsystem 624, bus 602 and or data feeds 624 and/orthe like as illustrated in FIG. 6 and described below) can includepartially de-optimized along the second QoS dimension in order to complywith the QoS expectations for the first QoS dimension.

At 360, there is a tradeoff between the first QoS dimension and thesecond QoS dimension. For example, the server system (e.g., usingprocessing unit 604, storage subsystem 618, system memory 610,communication subsystem 624, bus 602 and or data feeds 624 and/or thelike as illustrated in FIG. 6 and described below) can include tradeoffsbetween the first QoS dimension and the second QoS dimension.

In various embodiments, process 300 can include retrieving dataassociated with a historical output of a machine-learning model. Forexample, the server system (e.g., using processing unit 604, storagesubsystem 618, system memory 610, communication subsystem 624, bus 602and or data feeds 624 and/or the like as illustrated in FIG. 6 anddescribed below) can retrieve data associated with a historical outputof a machine-learning model as compared with a set of Quality of Servicemetrics and Key Performance Indicator Metrics, as described above.

In various embodiments, process 300 can include receiving one or moreinputs from an environment monitoring agent, wherein the environmentmonitoring agent receives information on at least one of: resources of asystem, concepts of the machine-learning model, data corruption, anddata availability to the machine-learning model. For example, the serversystem (e.g., using processing unit 604, storage subsystem 618, systemmemory 610, communication subsystem 624, bus 602 and or data feeds 624and/or the like as illustrated in FIG. 6 and described below) canreceive one or more inputs from an environment monitoring agent, asdescribed above. In some implementations, the environment monitoringagent receives information on at least one of: resources of a system,concepts of the machine-learning model, data corruption, and dataavailability to the machine-learning model.

In various embodiments, process 300 can include determining a change inat least one of: the resources of the system, the concepts of themachine-learning model, the data corruption, and the data availabilityto the machine-learning model. For example, the server system (e.g.,using processing unit 604, storage subsystem 618, system memory 610,communication subsystem 624, bus 602 and or data feeds 624 and/or thelike as illustrated in FIG. 6 and described below) can determine achange in at least one of: the resources of the system, the concepts ofthe machine-learning model, the data corruption, and the dataavailability to the machine-learning model, as described above. Forexample, the system can detect the loss of several processing units. Inanother example, the system can detect data corruption in the clientdata. In other examples, new customer data, potentially new types ofdata, may become available during the model execution.

In various embodiments, process 300 can include determining whether thechange in the at least one of the resources of the system, the conceptsof the machine-learning model, the data corruption, and the dataavailability to the machine-learning model will cause a predicted outputof the machine-learning model to vary more than a predetermined amount.For example, the server system (e.g., using processing unit 604, storagesubsystem 618, system memory 610, communication subsystem 624, bus 602and or data feeds 624 and/or the like as illustrated in FIG. 6 anddescribed below) can determine whether the change in the at least one ofthe resources of the system, the concepts of the machine-learning model,the data corruption, and the data availability to the machine-learningmodel will cause a predicted output of the machine-learning model tovary more than a predetermined amount, as described above. In some casesthe predetermined amount may be a percentage difference (i.e., 10%) froma historical output. In other cases, the predetermined amount can becompared to KPIs or QoS metrics.

In various embodiments, process 300 can include when the change in theat least one of the resources of the system, the concepts of themachine-learning model, the data corruption, and the data availabilityto the machine-learning model will cause the predicted output of themachine-learning model to vary more than a predetermined amount,identifying one or more remedial measures to the machine-learning modelto correct for the change. For example, the server system (e.g., usingprocessing unit 604, storage subsystem 618, system memory 610,communication subsystem 624, bus 602 and or data feeds 624 and/or thelike as illustrated in FIG. 6 and described below) can when the changein the at least one of the resources of the system, the concepts of themachine-learning model, the data corruption, and the data availabilityto the machine-learning model will cause the predicted output of themachine-learning model to vary more than a predetermined amount,identifying one or more remedial measures to the machine-learning modelto correct for the change, as described above. The system can include aplurality of remedial measures stored. The remedial measures can becoded with metadata that identifies one or more changes that theremedial measures can be used.

In various embodiments, process 300 can include displaying an alert tonotify a user of the change in the at least one of the resources of thesystem, the concepts of the machine-learning model, the data corruption,and the data availability to the machine-learning model and the one ormore remedial measures. For example, the server system (e.g., usingprocessing unit 604, storage subsystem 618, system memory 610,communication subsystem 624, bus 602 and or data feeds 624 and/or thelike as illustrated in FIG. 6 and described below) can display an alertto notify a user of the change in the at least one of the resources ofthe system, the concepts of the machine-learning model, the datacorruption, and the data availability to the machine-learning model andthe one or more remedial measures, as described above.

Process 300 can include additional implementations, such as any singleimplementation or any combination of implementations described belowand/or in connection with one or more other processes describedelsewhere herein. It should be appreciated that the specific stepsillustrated in FIG. 3 provide particular techniques for techniques forservice execution and monitoring for run-time service compositionaccording to various embodiments of the present disclosure. Othersequences of steps can also be performed according to alternativeembodiments. For example, alternative embodiments of the presentdisclosure can perform the steps outlined above in a different order.Moreover, the individual steps illustrated in FIG. 3 can includemultiple sub-steps that can be performed in various sequences asappropriate to the individual step. Furthermore, additional steps can beadded or removed depending on the particular applications. One ofordinary skill in the art would recognize many variations,modifications, and alternatives.

In some implementations, the predicted output includes at least one offirst metrics related to a performance of the multi-objectiveoptimization model in relation to Quality of Service parameters andsecond metrics related to predictions of the multi-objectiveoptimization model as compared with the historical output of themulti-objective optimization model.

In some implementations, process 300 includes executing the one or moreremedial measures to the machine-learning model to correct for thechange.

In some implementations, the resources of the system comprises at leastone of available memory, processing nodes, and network bandwidth.

In some implementations, the concepts measure a statistical distributionof a performance of the machine-learning model.

In some implementations, the data availability includes new data for oneor more new features.

In some implementations, the one or more remedial measures to themachine-learning model includes reducing a complexity of themachine-learning model.

In some implementations, the one or more remedial measures to themachine-learning model includes eliminating one or more featuresaffected by the data corruption.

In some implementations, the one or more remedial measures to themachine-learning model includes evaluating impact of new features on thepredict output.

In some implementations, the one or more remedial measures to themachine-learning model includes rolling back the machine-learning modelto a previous version.

In some implementations, the one or more remedial measures includes atleast one of composing a new model pipeline and replacing amachine-learning model micro-service.

In various embodiments, a server device can include one or morememories; and one or more processors in communication with the one ormore memories and configured to execute instructions stored in the oneor more memories to performing operations of a method described above.

In various embodiments, a computer-readable medium storing a pluralityof instructions that, when executed by one or more processors of acomputing device, cause the one or more processors to perform operationsof any of the methods described above.

Although FIG. 3 shows example steps of process 300, in someimplementations, process 300 can include additional steps, fewer steps,different steps, or differently arranged steps than those depicted inFIG. 3. Additionally, or alternatively, two or more of the steps ofprocess 300 can be performed in parallel.

III. Exemplary Hardware and Software Configurations

FIG. 4 depicts a simplified diagram of a distributed system 400 forimplementing one of the embodiments. In the illustrated embodiment,distributed system 400 includes one or more client computing devices402, 404, 406, and 408, which are configured to execute and operate aclient application such as a web browser, proprietary client (e.g.,Oracle Forms), or the like over one or more network(s) 410. Server 412may be communicatively coupled with remote client computing devices 402,404, 406, and 408 via network 410.

In various embodiments, server 412 may be adapted to run one or moreservices or software applications provided by one or more of thecomponents of the system. In some embodiments, these services may beoffered as web-based or cloud services or under a Software as a Service(SaaS) model to the users of client computing devices 402, 404, 406,and/or 408. Users operating client computing devices 402, 404, 406,and/or 408 may in turn utilize one or more client applications tointeract with server 412 to utilize the services provided by thesecomponents.

In the configuration depicted in the figure, the software components418, 420 and 422 of system 400 are shown as being implemented on server412. In other embodiments, one or more of the components of system 400and/or the services provided by these components may also be implementedby one or more of the client computing devices 402, 404, 406, and/or408. Users operating the client computing devices may then utilize oneor more client applications to use the services provided by thesecomponents. These components may be implemented in hardware, firmware,software, or combinations thereof. It should be appreciated that variousdifferent system configurations are possible, which may be differentfrom distributed system 400. The embodiment shown in the figure is thusone example of a distributed system for implementing an embodimentsystem and is not intended to be limiting.

Client computing devices 402, 404, 406, and/or 408 may be portablehandheld devices (e.g., an iPhone®, cellular telephone, an iPad®,computing tablet, a personal digital assistant (PDA)) or wearabledevices (e.g., a Google Glass® head mounted display), running softwaresuch as Microsoft Windows Mobile®, and/or a variety of mobile operatingsystems such as iOS, Windows Phone, Android, BlackBerry 10, Palm OS, andthe like, and being Internet, e-mail, short message service (SMS),Blackberry®, or other communication protocol enabled. The clientcomputing devices can be general purpose personal computers including,by way of example, personal computers and/or laptop computers runningvarious versions of Microsoft Windows®, Apple Macintosh®, and/or Linuxoperating systems. The client computing devices can be workstationcomputers running any of a variety of commercially-available UNIX® orUNIX-like operating systems, including without limitation the variety ofGNU/Linux operating systems, such as for example, Google Chrome OS.Alternatively, or in addition, client computing devices 402, 404, 406,and 408 may be any other electronic device, such as a thin-clientcomputer, an Internet-enabled gaming system (e.g., a Microsoft Xboxgaming console with or without a Kinect® gesture input device), and/or apersonal messaging device, capable of communicating over network(s) 410.

Although exemplary distributed system 400 is shown with four clientcomputing devices, any number of client computing devices may besupported. Other devices, such as devices with sensors, etc., mayinteract with server 412.

Network(s) 410 in distributed system 400 may be any type of networkfamiliar to those skilled in the art that can support datacommunications using any of a variety of commercially-availableprotocols, including without limitation TCP/IP (transmission controlprotocol/Internet protocol), SNA (systems network architecture), IPX(Internet packet exchange), AppleTalk, and the like. Merely by way ofexample, network(s) 410 can be a local area network (LAN), such as onebased on Ethernet, Token-Ring and/or the like. Network(s) 410 can be awide-area network and the Internet. It can include a virtual network,including without limitation a virtual private network (VPN), anintranet, an extranet, a public switched telephone network (PSTN), aninfra-red network, a wireless network (e.g., a network operating underany of the Institute of Electrical and Electronics (IEEE) 802.11 suiteof protocols, Bluetooth®, and/or any other wireless protocol); and/orany combination of these and/or other networks.

Server 412 may be composed of one or more general purpose computers,specialized server computers (including, by way of example, PC (personalcomputer) servers, UNIX® servers, mid-range servers, mainframecomputers, rack-mounted servers, etc.), server farms, server clusters,or any other appropriate arrangement and/or combination. In variousembodiments, server 412 may be adapted to run one or more services orsoftware applications described in the foregoing disclosure. Forexample, server 412 may correspond to a server for performing processingdescribed above according to an embodiment of the present disclosure.

Server 412 may run an operating system including any of those discussedabove, as well as any commercially available server operating system.Server 412 may also run any of a variety of additional serverapplications and/or mid-tier applications, including HTTP (hypertexttransport protocol) servers, FTP (file transfer protocol) servers, CGI(common gateway interface) servers, JAVA® servers, database servers, andthe like. Exemplary database servers include without limitation thosecommercially available from Oracle, Microsoft, Sybase, IBM(International Business Machines), and the like.

In some implementations, server 412 may include one or more applicationsto analyze and consolidate data feeds and/or event updates received fromusers of client computing devices 402, 404, 406, and 408. As an example,data feeds and/or event updates may include, but are not limited to,Twitter® feeds, Facebook® updates or real-time updates received from oneor more third party information sources and continuous data streams,which may include real-time events related to sensor data applications,financial tickers, network performance measuring tools (e.g., networkmonitoring and traffic management applications), clickstream analysistools, automobile traffic monitoring, and the like. Server 412 may alsoinclude one or more applications to display the data feeds and/orreal-time events via one or more display devices of client computingdevices 402, 404, 406, and 408.

Distributed system 400 may also include one or more databases 414 and416. Databases 414 and 416 may reside in a variety of locations. By wayof example, one or more of databases 414 and 416 may reside on anon-transitory storage medium local to (and/or resident in) server 412.Alternatively, databases 414 and 416 may be remote from server 412 andin communication with server 412 via a network-based or dedicatedconnection. In one set of embodiments, databases 414 and 416 may residein a storage-area network (SAN). Similarly, any necessary files forperforming the functions attributed to server 412 may be stored locallyon server 412 and/or remotely, as appropriate. In one set ofembodiments, databases 414 and 416 may include relational databases,such as databases provided by Oracle, that are adapted to store, update,and retrieve data in response to SQL-formatted commands.

FIG. 5 is a simplified block diagram of one or more components of asystem environment 500 by which services provided by one or morecomponents of an embodiment system may be offered as cloud services, inaccordance with an embodiment of the present disclosure. In theillustrated embodiment, system environment 500 includes one or moreclient computing devices 504, 506, and 508 that may be used by users tointeract with a cloud infrastructure system 502 that provides cloudservices. The client computing devices may be configured to operate aclient application such as a web browser, a proprietary clientapplication (e.g., Oracle Forms), or some other application, which maybe used by a user of the client computing device to interact with cloudinfrastructure system 502 to use services provided by cloudinfrastructure system 502.

It should be appreciated that cloud infrastructure system 502 depictedin the figure may have other components than those depicted. Further,the embodiment shown in the figure is only one example of a cloudinfrastructure system that may incorporate an embodiment of thedisclosure. In some other embodiments, cloud infrastructure system 502may have more or fewer components than shown in the figure, may combinetwo or more components, or may have a different configuration orarrangement of components.

Client computing devices 504, 506, and 508 may be devices similar tothose described above for 402, 404, 406, and 408.

Although exemplary system environment 500 is shown with three clientcomputing devices, any number of client computing devices may besupported. Other devices such as devices with sensors, etc. may interactwith cloud infrastructure system 502.

Network(s) 510 may facilitate communications and exchange of databetween clients 504, 506, and 508 and cloud infrastructure system 502.Each network may be any type of network familiar to those skilled in theart that can support data communications using any of a variety ofcommercially available protocols, including those described above fornetwork(s) 410.

Cloud infrastructure system 502 may comprise one or more computersand/or servers that may include those described above for server 412.

In certain embodiments, services provided by the cloud infrastructuresystem may include a host of services that are made available to usersof the cloud infrastructure system on demand, such as online datastorage and backup solutions, Web-based e-mail services, hosted officesuites and document collaboration services, database processing, managedtechnical support services, and the like. Services provided by the cloudinfrastructure system can dynamically scale to meet the needs of itsusers. A specific instantiation of a service provided by cloudinfrastructure system is referred to herein as a “service instance.” Ingeneral, any service made available to a user via a communicationnetwork, such as the Internet, from a cloud service provider's system isreferred to as a “cloud service.” Typically, in a public cloudenvironment, servers and systems that make up the cloud serviceprovider's system are different from the customer's own on-premisesservers and systems. For example, a cloud service provider's system mayhost an application, and a user may, via a communication network such asthe Internet, on demand, order and use the application.

In some examples, a service in a computer network cloud infrastructuremay include protected computer network access to storage, a hosteddatabase, a hosted web server, a software application, or other serviceprovided by a cloud vendor to a user, or as otherwise known in the art.For example, a service can include password-protected access to remotestorage on the cloud through the Internet. As another example, a servicecan include a web service-based hosted relational database and ascript-language middleware engine for private use by a networkeddeveloper. As another example, a service can include access to an emailsoftware application hosted on a cloud vendor's web site.

In certain embodiments, cloud infrastructure system 502 may include asuite of applications, middleware, and database service offerings thatare delivered to a customer in a self-service, subscription-based,elastically scalable, reliable, highly available, and secure manner. Anexample of such a cloud infrastructure system is the Oracle Public Cloudprovided by the present assignee.

In various embodiments, cloud infrastructure system 502 may be adaptedto automatically provision, manage and track a customer's subscriptionto services offered by cloud infrastructure system 502. Cloudinfrastructure system 502 may provide the cloud services via differentdeployment models. For example, services may be provided under a publiccloud model in which cloud infrastructure system 502 is owned by anorganization selling cloud services (e.g., owned by Oracle) and theservices are made available to the general public or different industryenterprises. As another example, services may be provided under aprivate cloud model in which cloud infrastructure system 502 is operatedsolely for a single organization and may provide services for one ormore entities within the organization. The cloud services may also beprovided under a community cloud model in which cloud infrastructuresystem 502 and the services provided by cloud infrastructure system 502are shared by several organizations in a related community. The cloudservices may also be provided under a hybrid cloud model, which is acombination of two or more different models.

In some embodiments, the services provided by cloud infrastructuresystem 530 may include one or more services provided under Software as aService (SaaS) category, Platform as a Service (PaaS) category,Infrastructure as a Service (IaaS) category, or other categories ofservices including hybrid services. A customer, via a subscriptionorder, may order one or more services provided by cloud infrastructuresystem 502. Cloud infrastructure system 502 then performs processing toprovide the services in the customer's subscription order.

In some embodiments, the services provided by cloud infrastructuresystem 502 may include, without limitation, application services,platform services and infrastructure services. In some examples,application services may be provided by the cloud infrastructure systemvia a SaaS platform. The SaaS platform may be configured to providecloud services that fall under the SaaS category. For example, the SaaSplatform may provide capabilities to build and deliver a suite ofon-demand applications on an integrated development and deploymentplatform. The SaaS platform may manage and control the underlyingsoftware and infrastructure for providing the SaaS services. Byutilizing the services provided by the SaaS platform, customers canutilize applications executing on the cloud infrastructure system.Customers can acquire the application services without the need forcustomers to purchase separate licenses and support. Various differentSaaS services may be provided. Examples include, without limitation,services that provide solutions for sales performance management,enterprise integration, and flexibility for large organizations.

In some embodiments, platform services may be provided by the cloudinfrastructure system via a PaaS platform. The PaaS platform may beconfigured to provide cloud services that fall under the PaaS category.Examples of platform services may include without limitation servicesthat enable organizations (such as Oracle) to consolidate existingapplications on a shared, common architecture, as well as the ability tobuild new applications that leverage the shared services provided by theplatform. The PaaS platform may manage and control the underlyingsoftware and infrastructure for providing the PaaS services. Customerscan acquire the PaaS services provided by the cloud infrastructuresystem without the need for customers to purchase separate licenses andsupport. Examples of platform services include, without limitation,Oracle Java Cloud Service (JCS), Oracle Database Cloud Service (DBCS),and others.

By utilizing the services provided by the PaaS platform, customers canemploy programming languages and tools supported by the cloudinfrastructure system and also control the deployed services. In someembodiments, platform services provided by the cloud infrastructuresystem may include database cloud services, middleware cloud services(e.g., Oracle Fusion Middleware services), and Java cloud services. Inone embodiment, database cloud services may support shared servicedeployment models that enable organizations to pool database resourcesand offer customers a Database as a Service in the form of a databasecloud. Middleware cloud services may provide a platform for customers todevelop and deploy various cloud applications, and Java cloud servicesmay provide a platform for customers to deploy Java applications, in thecloud infrastructure system.

Various different infrastructure services may be provided by an IaaSplatform in the cloud infrastructure system. The infrastructure servicesfacilitate the management and control of the underlying computingresources, such as storage, networks, and other fundamental computingresources for customers utilizing services provided by the SaaS platformand the PaaS platform.

In certain embodiments, cloud infrastructure system 502 may also includeinfrastructure resources 530 for providing the resources used to providevarious services to customers of the cloud infrastructure system. In oneembodiment, infrastructure resources 530 may include pre-integrated andoptimized combinations of hardware, such as servers, storage, andnetworking resources to execute the services provided by the PaaSplatform and the SaaS platform.

In some embodiments, resources in cloud infrastructure system 502 may beshared by multiple users and dynamically re-allocated per demand.Additionally, resources may be allocated to users in different timezones. For example, cloud infrastructure system 530 may enable a firstset of users in a first time zone to utilize resources of the cloudinfrastructure system for a specified number of hours and then enablethe re-allocation of the same resources to another set of users locatedin a different time zone, thereby maximizing the utilization ofresources.

In certain embodiments, a number of internal shared services 532 may beprovided that are shared by different components or modules of cloudinfrastructure system 502 and by the services provided by cloudinfrastructure system 502. These internal shared services may include,without limitation, a security and identity service, an integrationservice, an enterprise repository service, an enterprise managerservice, a virus scanning and white list service, a high availability,backup and recovery service, service for enabling cloud support, anemail service, a notification service, a file transfer service, and thelike.

In certain embodiments, cloud infrastructure system 502 may providecomprehensive management of cloud services (e.g., SaaS, PaaS, and IaaSservices) in the cloud infrastructure system. In one embodiment, cloudmanagement functionality may include capabilities for provisioning,managing and tracking a customer's subscription received by cloudinfrastructure system 502, and the like.

In one embodiment, as depicted in the figure, cloud managementfunctionality may be provided by one or more modules, such as an ordermanagement module 520, an order orchestration module 522, an orderprovisioning module 524, an order management and monitoring module 526,and an identity management module 528. These modules may include or beprovided using one or more computers and/or servers, which may begeneral purpose computers, specialized server computers, server farms,server clusters, or any other appropriate arrangement and/orcombination.

In exemplary operation 534, a customer using a client device, such asclient device 504, 506 or 508, may interact with cloud infrastructuresystem 502 by requesting one or more services provided by cloudinfrastructure system 502 and placing an order for a subscription forone or more services offered by cloud infrastructure system 502. Incertain embodiments, the customer may access a cloud User Interface(UI), cloud UI 512, cloud UI 514 and/or cloud UI 516 and place asubscription order via these UIs. The order information received bycloud infrastructure system 502 in response to the customer placing anorder may include information identifying the customer and one or moreservices offered by the cloud infrastructure system 502 that thecustomer intends to subscribe to.

After an order has been placed by the customer, the order information isreceived via the cloud UIs, 512, 514 and/or 516.

At operation 536, the order is stored in order database 518. Orderdatabase 518 can be one of several databases operated by cloudinfrastructure system and operated in conjunction with other systemelements.

At operation 538, the order information is forwarded to an ordermanagement module 520. In some instances, order management module 520may be configured to perform billing and accounting functions related tothe order, such as verifying the order, and upon verification, bookingthe order.

At operation 540, information regarding the order is communicated to anorder orchestration module 522. Order orchestration module 522 mayutilize the order information to orchestrate the provisioning ofservices and resources for the order placed by the customer. In someinstances, order orchestration module 522 may orchestrate theprovisioning of resources to support the subscribed services using theservices of order provisioning module 524.

In certain embodiments, order orchestration module 522 enables themanagement of processes associated with each order and applies logic todetermine whether an order should proceed to provisioning. At operation542, upon receiving an order for a new subscription, order orchestrationmodule 522 sends a request to order provisioning module 524 to allocateresources and configure those resources needed to fulfill thesubscription order. Order provisioning module 524 enables the allocationof resources for the services ordered by the customer. Orderprovisioning module 524 provides a level of abstraction between thecloud services provided by cloud infrastructure system 500 and thephysical implementation layer that is used to provision the resourcesfor providing the requested services. Order orchestration module 522 maythus be isolated from implementation details, such as whether or notservices and resources are actually provisioned on the fly orpre-provisioned and only allocated/assigned upon request.

At operation 544, once the services and resources are provisioned, anotification of the provided service may be sent to customers on clientdevices 504, 506 and/or 508 by order provisioning module 524 of cloudinfrastructure system 502.

At operation 546, the customer's subscription order may be managed andtracked by an order management and monitoring module 526. In someinstances, order management and monitoring module 526 may be configuredto collect usage statistics for the services in the subscription order,such as the amount of storage used, the amount data transferred, thenumber of users, and the amount of system up time and system down time.

In certain embodiments, cloud infrastructure system 500 may include anidentity management module 528. Identity management module 528 may beconfigured to provide identity services, such as access management andauthorization services in cloud infrastructure system 500. In someembodiments, identity management module 528 may control informationabout customers who wish to utilize the services provided by cloudinfrastructure system 502. Such information can include information thatauthenticates the identities of such customers and information thatdescribes which actions those customers are authorized to performrelative to various system resources (e.g., files, directories,applications, communication ports, memory segments, etc.) Identitymanagement module 528 may also include the management of descriptiveinformation about each customer and about how and by whom thatdescriptive information can be accessed and modified.

FIG. 6 illustrates an exemplary computer system 600, in which variousembodiments of the present disclosure may be implemented. The system 600may be used to implement any of the computer systems described above. Asshown in the figure, computer system 600 includes a processing unit 604that communicates with a number of peripheral subsystems via a bussubsystem 602. These peripheral subsystems may include a processingacceleration unit 606, an input/output (I/O) subsystem 608, a storagesubsystem 618 and a communications subsystem 624. Storage subsystem 618includes tangible computer-readable storage media 622 and a systemmemory 610.

Bus subsystem 602 provides a mechanism for letting the variouscomponents and subsystems of computer system 600 communicate with eachother as intended. Although bus subsystem 602 is shown schematically asa single bus, alternative embodiments of the bus subsystem may utilizemultiple buses. Bus subsystem 602 may be any of several types of busstructures including a memory bus or memory controller, a peripheralbus, and a local bus using any of a variety of bus architectures. Forexample, such architectures may include an Industry StandardArchitecture (ISA) bus, Micro Channel Architecture (MCA) bus, EnhancedISA (EISA) bus, Video Electronics Standards Association (VESA) localbus, and Peripheral Component Interconnect (PCI) bus, which can beimplemented as a Mezzanine bus manufactured to the IEEE P1386.1standard.

Processing unit 604, which can be implemented as one or more integratedcircuits (e.g., a conventional microprocessor or microcontroller),controls the operation of computer system 600. One or more processorsmay be included in processing unit 604. These processors may includesingle core or multicore processors. In certain embodiments, processingunit 604 may be implemented as one or more independent processing units632 and/or 634 with single or multicore processors included in eachprocessing unit. In other embodiments, processing unit 604 may also beimplemented as a quad-core processing unit formed by integrating twodual-core processors into a single chip.

In various embodiments, processing unit 604 can execute a variety ofprograms in response to program code and can maintain multipleconcurrently executing programs or processes. At any given time, some orall of the program code to be executed can be resident in processingunit 604 and/or in storage subsystem 618. Through suitable programming,processing unit 604 can provide various functionalities described above.Computer system 600 may additionally include a processing accelerationunit 606, which can include a digital signal processor (DSP), aspecial-purpose processor, and/or the like.

I/O subsystem 608 may include user interface input devices and userinterface output devices. User interface input devices may include akeyboard, pointing devices such as a mouse or trackball, a touchpad ortouch screen incorporated into a display, a scroll wheel, a click wheel,a dial, a button, a switch, a keypad, audio input devices with voicecommand recognition systems, microphones, and other types of inputdevices. User interface input devices may include, for example, motionsensing and/or gesture recognition devices such as the Microsoft Kinect®motion sensor that enables users to control and interact with an inputdevice, such as the Microsoft Xbox® 360 game controller, through anatural user interface using gestures and spoken commands. Userinterface input devices may also include eye gesture recognition devicessuch as the Google Glass® blink detector that detects eye activity(e.g., ‘blinking’ while taking pictures and/or making a menu selection)from users and transforms the eye gestures as input into an input device(e.g., Google Glass®). Additionally, user interface input devices mayinclude voice recognition sensing devices that enable users to interactwith voice recognition systems (e.g., Siri® navigator), through voicecommands.

User interface input devices may also include, without limitation, threedimensional (3D) mice, joysticks or pointing sticks, gamepads andgraphic tablets, and audio/visual devices such as speakers, digitalcameras, digital camcorders, portable media players, webcams, imagescanners, fingerprint scanners, barcode reader 3D scanners, 3D printers,laser rangefinders, and eye gaze tracking devices. Additionally, userinterface input devices may include, for example, medical imaging inputdevices such as computed tomography, magnetic resonance imaging,position emission tomography, medical ultrasonography devices. Userinterface input devices may also include, for example, audio inputdevices such as musical instrument digital interface (MIDI) keyboards,digital musical instruments and the like.

User interface output devices may include a display subsystem, indicatorlights, or non-visual displays such as audio output devices, etc. Thedisplay subsystem may be a cathode ray tube (CRT), a flat-panel device,such as that using a liquid crystal display (LCD) or plasma display, aprojection device, a touch screen, and the like. In general, use of theterm “output device” is intended to include all possible types ofdevices and mechanisms for outputting information from computer system600 to a user or other computer. For example, user interface outputdevices may include, without limitation, a variety of display devicesthat visually convey text, graphics and audio/video information such asmonitors, printers, speakers, headphones, automotive navigation systems,plotters, voice output devices, and modems.

Computer system 600 may comprise a storage subsystem 618 that comprisessoftware elements, shown as being currently located within a systemmemory 610. System memory 610 may store program instructions that areloadable and executable on processing unit 604, as well as datagenerated during the execution of these programs.

Depending on the configuration and type of computer system 600, systemmemory 610 may be volatile (such as random access memory (RAM)) and/ornon-volatile (such as read-only memory (ROM), flash memory, etc.) TheRAM typically contains data and/or program modules that are immediatelyaccessible to and/or presently being operated and executed by processingunit 604. In some implementations, system memory 610 may includemultiple different types of memory, such as static random access memory(SRAM) or dynamic random access memory (DRAM). In some implementations,a basic input/output system (BIOS), containing the basic routines thathelp to transfer information between elements within computer system600, such as during start-up, may typically be stored in the ROM. By wayof example, and not limitation, system memory 610 also illustratesapplication programs 612, which may include client applications, Webbrowsers, mid-tier applications, relational database management systems(RDBMS), etc., program data 614, and an operating system 616. By way ofexample, operating system 616 may include various versions of MicrosoftWindows®, Apple Macintosh®, and/or Linux operating systems, a variety ofcommercially-available UNIX® or UNIX-like operating systems (includingwithout limitation the variety of GNU/Linux operating systems, theGoogle Chrome® OS, and the like) and/or mobile operating systems such asiOS, Windows® Phone, Android® OS, BlackBerry® 10 OS, and Palm® OSoperating systems.

Storage subsystem 618 may also provide a tangible computer-readablestorage medium for storing the basic programming and data constructsthat provide the functionality of some embodiments. Software (programs,code modules, instructions) that when executed by a processor providethe functionality described above may be stored in storage subsystem618. These software modules or instructions may be executed byprocessing unit 604. Storage subsystem 618 may also provide a repositoryfor storing data used in accordance with the present disclosure.

Storage subsystem 618 may also include a computer-readable storage mediareader 620 that can further be connected to computer-readable storagemedia 622. Together and, optionally, in combination with system memory610, computer-readable storage media 622 may comprehensively representremote, local, fixed, and/or removable storage devices plus storagemedia for temporarily and/or more permanently containing, storing,transmitting, and retrieving computer-readable information.

Computer-readable storage media 622 containing code, or portions ofcode, can also include any appropriate media known or used in the art,including storage media and communication media, such as but not limitedto, volatile and non-volatile, removable and non-removable mediaimplemented in any method or technology for storage and/or transmissionof information. This can include tangible computer-readable storagemedia such as RAM, ROM, electronically erasable programmable ROM(EEPROM), flash memory or other memory technology, compactdisc-read-only memory (CD-ROM), digital versatile disk (DVD), or otheroptical storage, magnetic cassettes, magnetic tape, magnetic diskstorage or other magnetic storage devices, or other tangible computerreadable media. This can also include nontangible computer-readablemedia, such as data signals, data transmissions, or any other mediumwhich can be used to transmit the desired information and which can beaccessed by computing system 600.

By way of example, computer-readable storage media 622 may include ahard disk drive that reads from or writes to non-removable, nonvolatilemagnetic media, a magnetic disk drive that reads from or writes to aremovable, nonvolatile magnetic disk, and an optical disk drive thatreads from or writes to a removable, nonvolatile optical disk such as aCD ROM, DVD, and Blu-Ray® disk, or other optical media.Computer-readable storage media 622 may include, but is not limited to,Zip® drives, flash memory cards, universal serial bus (USB) flashdrives, secure digital (SD) cards, DVD disks, digital video tape, andthe like. Computer-readable storage media 622 may also include,solid-state drives (SSD) based on non-volatile memory such asflash-memory based SSDs, enterprise flash drives, solid state ROM, andthe like, SSDs based on volatile memory such as solid state RAM, dynamicRAM, static RAM, dynamic random access memory (DRAM)-based SSDs,magnetoresistive RAM (MRAM) SSDs, and hybrid SSDs that use a combinationof DRAM and flash memory based SSDs. The disk drives and theirassociated computer-readable media may provide non-volatile storage ofcomputer-readable instructions, data structures, program modules, andother data for computer system 600.

Communications subsystem 624 provides an interface to other computersystems and networks. Communications subsystem 624 serves as aninterface for receiving data from and transmitting data to other systemsfrom computer system 600. For example, communications subsystem 624 mayenable computer system 600 to connect to one or more devices via theInternet. In some embodiments communications subsystem 624 can includeradio frequency (RF) transceiver components for accessing wireless voiceand/or data networks (e.g., using cellular telephone technology,advanced data network technology, such as 3G, 4G or EDGE (enhanced datarates for global evolution), Wi-Fi (IEEE 1202.11 family standards, orother mobile communication technologies, or any combination thereof),global positioning system (GPS) receiver components, and/or othercomponents. In some embodiments communications subsystem 624 can providewired network connectivity (e.g., Ethernet) in addition to or instead ofa wireless interface.

In some embodiments, communications subsystem 624 may also receive inputcommunication in the form of structured and/or unstructured data feeds626, event streams 628, event updates 630, and the like on behalf of oneor more users who may use computer system 600.

By way of example, communications subsystem 624 may be configured toreceive data feeds 626 in real-time from users of social networks and/orother communication services such as Twitter® feeds, Facebook® updates,web feeds such as Rich Site Summary (RSS) feeds, and/or real-timeupdates from one or more third party information sources.

Additionally, communications subsystem 624 may also be configured toreceive data in the form of continuous data streams, which may includeevent streams 628 of real-time events and/or event updates 630, that maybe continuous or unbounded in nature with no explicit end. Examples ofapplications that generate continuous data may include, for example,sensor data applications, financial tickers, network performancemeasuring tools (e.g. network monitoring and traffic managementapplications), clickstream analysis tools, automobile trafficmonitoring, and the like.

Communications subsystem 624 may also be configured to output thestructured and/or unstructured data feeds 626, event streams 628, eventupdates 630, and the like to one or more databases that may be incommunication with one or more streaming data source computers coupledto computer system 600.

Computer system 600 can be one of various types, including a handheldportable device (e.g., an iPhone® cellular phone, an iPad® computingtablet, a PDA), a wearable device (e.g., a Google Glass® head mounteddisplay), a PC, a workstation, a mainframe, a kiosk, a server rack, orany other data processing system.

Due to the ever-changing nature of computers and networks, thedescription of computer system 600 depicted in the figure is intendedonly as a specific example. Many other configurations having more orfewer components than the system depicted in the figure are possible.For example, customized hardware might also be used and/or particularelements might be implemented in hardware, firmware, software (includingapplets), or a combination. Further, connection to other computingdevices, such as network input/output devices, may be employed. Based onthe disclosure and teachings provided herein, a person of ordinary skillin the art will appreciate other ways and/or methods to implement thevarious embodiments.

In the foregoing specification, aspects of the disclosure are describedwith reference to specific embodiments thereof, but those skilled in theart will recognize that the disclosure is not limited thereto. Variousfeatures and aspects of the above-described disclosure may be usedindividually or jointly. Further, embodiments can be utilized in anynumber of environments and applications beyond those described hereinwithout departing from the broader spirit and scope of thespecification. The specification and drawings are, accordingly, to beregarded as illustrative rather than restrictive.

What is claimed is:
 1. A method for automating a run-time adaption of amulti-objective optimization model in a software architecture, themethod comprising: receiving two or more Quality of Service (QoS)dimensions for the multi-objective optimization model, wherein the twoor more QoS dimensions include at least a first QoS dimension and asecond QoS dimension; and maximizing the multi-objective optimizationmodel along the first QoS dimension, wherein the maximizing includesselecting one or more pipelines for the multi-objective optimizationmodel in the software architecture that meet QoS expectations specifiedfor the first QoS dimension and the second QoS dimension, wherein anordering of the pipelines is dependent on which QoS dimensions wereoptimized and de-optimized and to what extent, wherein themulti-objective optimization model is partially de-optimized along thesecond QoS dimension in order to comply with the QoS expectations forthe first QoS dimension, and whereby there is a tradeoff between thefirst QoS dimension and the second QoS dimension.
 2. The method of claim1, further comprising: retrieving data associated with a historicaloutput of the multi-objective optimization model; receiving one or moreinputs from an environment-monitoring agent, wherein theenvironment-monitoring agent receives information on at least one of:resources of a system, concepts of the multi-objective optimizationmodel, data corruption, and data availability to the multi-objectiveoptimization model; determining a change in at least one of: theresources of the system, the concepts of the multi-objectiveoptimization model, the data corruption, and the data availability tothe multi-objective optimization model; determining whether the changein the at least one of the resources of the system, the concepts of themulti-objective optimization model, the data corruption, and the dataavailability to the multi-objective optimization model will cause apredicted output of the multi-objective optimization model to vary morethan a predetermined amount; when the change in the at least one of theresources of the system, the concepts of the multi-objectiveoptimization model, the data corruption, and the data availability tothe multi-objective optimization model cause the predicted output of themulti-objective optimization model to vary more than a predeterminedamount, identifying one or more remedial measures to the multi-objectiveoptimization model to correct for the change; and displaying an alert tonotify a user of the change in the at least one of the resources of thesystem, the concepts of the multi-objective optimization model, the datacorruption, and the data availability to the multi-objectiveoptimization model and the one or more remedial measures.
 3. The methodof claim 2, wherein the predicted output includes at least one of firstmetrics related to a performance of the multi-objective optimizationmodel in relation to Quality of Service parameters and second metricsrelated to predictions of the multi-objective optimization model ascompared with the historical output of the multi-objective optimizationmodel.
 4. The method of claim 2, further comprising executing the one ormore remedial measures to the multi-objective optimization model tocorrect for the change.
 5. The method of claim 2, wherein the resourcesof the system comprises at least one of available memory, processingnodes, and network bandwidth.
 6. The method of claim 2, wherein theconcepts measure a statistical distribution of a performance of themulti-objective optimization model.
 7. The method of claim 2, whereinthe one or more remedial measures to the multi-objective optimizationmodel includes reducing a complexity of the multi-objective optimizationmodel.
 8. The method of claim 2, wherein the one or more remedialmeasures to the multi-objective optimization model includes eliminatingone or more features affected by the data corruption.
 9. The method ofclaim 2, wherein the one or more remedial measures to themulti-objective optimization model includes evaluating impact of newfeatures on the predict output.
 10. The method of claim 2, wherein theone or more remedial measures to the multi-objective optimization modelincludes rolling back the multi-objective optimization model to aprevious version.
 11. A non-transitory computer-readable medium storinginstructions for automating a run-time adaption of a multi-objectiveoptimization problem in a software architecture, the instructionscomprising: one or more instructions that, when executed by one or moreprocessors, cause the one or more processors to: receiving two or moreQuality of Service (QoS) dimensions for the multi-objective optimizationmodel, wherein the two or more QoS dimensions include at least a firstQoS dimension and a second QoS dimension; maximizing the multi-objectiveoptimization model along the first QoS dimension, wherein the maximizingincludes selecting one or more pipelines for the multi-objectiveoptimization model in the software architecture that meet QoSexpectations specified for the first QoS dimension and the second QoSdimension, wherein an ordering of the pipelines is dependent on whichQoS dimensions were optimized and de-optimized and to what extent,wherein the multi-objective optimization model is partially de-optimizedalong the second QoS dimension in order to comply with the QoSexpectations for the first QoS dimension, and whereby there is atradeoff between the first QoS dimension and the second QoS dimension.12. The non-transitory computer-readable medium of claim 11, furthercomprising instructions that, when executed by one or more processors,cause the one or more processors to: retrieve data associated with ahistorical output of a multi-objective optimization model; receive oneor more inputs from an environment-monitoring agent, wherein theenvironment-monitoring agent receives information on at least one of:resources of a system, concepts of the multi-objective optimizationmodel, data corruption, and data availability to the multi-objectiveoptimization model; determine a change in at least one of: the resourcesof the system, the concepts of the multi-objective optimization model,the data corruption, and the data availability to the multi-objectiveoptimization model; determine whether the change in the at least one ofthe resources of the system, the concepts of the multi-objectiveoptimization model, the data corruption, and the data availability tothe multi-objective optimization model will cause a predicted output ofthe multi-objective optimization model to vary more than a predeterminedamount; when the change in the at least one of the resources of thesystem, the concepts of the multi-objective optimization model, the datacorruption, and the data availability to the multi-objectiveoptimization model will cause the predicted output of themulti-objective optimization model to vary more than a predeterminedamount, identifying one or more remedial measures to the multi-objectiveoptimization model to correct for the change; and display an alert tonotify a user of the change in the at least one of the resources of thesystem, the concepts of the multi-objective optimization model, the datacorruption, and the data availability to the multi-objectiveoptimization model and the one or more remedial measures.
 13. Thenon-transitory computer-readable medium of claim 12, wherein thepredicted output includes at least one of first metrics related to aperformance of the multi-objective optimization model in relation toQuality of Service parameters and second metrics related to predictionsof the multi-objective optimization model as compared with thehistorical output of the multi-objective optimization model.
 14. Thenon-transitory computer-readable medium of claim 12, further comprisingexecuting the one or more remedial measures to the multi-objectiveoptimization model to correct for the change.
 15. The non-transitorycomputer-readable medium of claim 12, wherein the one or moreinstructions, that cause the one or more processors to the resources ofthe system, cause the one or more processors to at least one ofavailable memory, processing nodes, and network bandwidth.
 16. Thenon-transitory computer-readable medium of claim 12, wherein theconcepts measure a statistical distribution of a performance of themulti-objective optimization model.
 17. The non-transitorycomputer-readable medium of claim 12, wherein the data availabilityincludes new data for one or more new features.
 18. The non-transitorycomputer-readable medium of claim 12, wherein the one or more remedialmeasures to the multi-objective optimization model includes reducing acomplexity of the multi-objective optimization model.
 19. Thenon-transitory computer-readable medium of claim 12, wherein the one ormore remedial measures to the multi-objective optimization modelincludes eliminating one or more features affected by the datacorruption.
 20. The non-transitory computer-readable medium of claim 12,wherein the one or more remedial measures to the multi-objectiveoptimization model includes evaluating impact of new features on thepredict output.